I propose a program of training and research that will allow me to contribute my background in experimental and statistical physics and my experience in physics-based modeling and simulation of complex dynamical systems to pursue independent research in computational systems biology. I will focus my research on the structure and dynamics of biological signaling networks at multiple resolution scales. I will collaborate with my mentor, Elliott Ross, an experimental signaling biochemist, and members of the Alliance for Cellular Signaling, on two related research problems. I will also participate in graduate courses and research conferences related to this work. 1. I will extend and generalize a computational model of signaling in a G protein module (receptor, heterotrimeric G protein, GTPase-activating protein, effector) to evaluate how elementary interactions within the module combine to produce its characteristic kinetic and dynamic behaviors. Even a small network element of this kind already displays significant complexity, which I will address by developing stochastic tools to evaluate the parameter sets that satisfy the model. Such analysis will determine the quality and multiplicity of acceptable parameters sets and will indicate whether specific parameters are linked in a biologically meaningful way. To determine whether an implicitly mean-value ODE model can cope with the level of complexity displayed by this system, I will also develop and apply a stochastic dynamic model of the G protein module. 2. I will develop a set of models for the signaling pathway of the RAW264.7 macrophage, the research target of the Alliance. I will use this model to optimize the number and placement of intracellular probes of signaling activity. These probes will be used to determine dynamic signaling pathways activated by specific stimuli in the context of the overall topology of the signaling network. I will then develop theory to evaluate actual pathway utilization during signal transduction.